At The Data Incubator we run a free eight-week data science fellowship to help our Fellows land industry jobs. We love Fellows with diverse academic backgrounds that go beyond what companies traditionally think of when hiring data scientists. Xia was a Fellow in our Summer 2015 cohort who landed a job at LinkedIn.

Tell us about your background. How did it set you up to be a great data scientist?

I am an experimental physicist in soft condensed matter by training in my PhD program at Emory University. There are three things that I think have helped me a lot to become a good data scientist:1). The solid background in physics and math that I obtained back in my college. The knowledge itself isn’t necessarily reflected in my day to day work now. However, the training of logical thinking and critical thinking is really beneficial in a long run.2). Persistence in finding root causes. The massive amount of data can easily leave you feeling swamped. I believe that always asking why until you get to the true cause of the problem is really essential. Sometimes, the insights are hidden behind and need our motivation to dig them out. No matter if it’s driven by natural stubbornness or original curiosity, I find the persistence usually a great help for walking the last mile to the final discovery. 3). Passion for solving problems using data. There is a joint program in our department where I took computer science courses for a masters degree. In the course projects, I started to find my passion in solving practical problems using data science approaches. Now I am working on product analytics and I cannot imagine how tough it could be without that passion and curiosity about what we can do to improve it.